{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Interactive Variogram Modeling Demonstration\n",
    "\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "\n",
    "### The Interactive Workflow\n",
    "\n",
    "Here's a simple workflow on detecting the major spatial continuity directions in a spatial dataset with variogram analysis and then building a consistent. positive definate variogram model in 2D. \n",
    "\n",
    "* this model will provide consistent variogram values for any offset (azimuth and distance) in 2D\n",
    "\n",
    "This information is essential to optimum well placement and prectiction away from wells.  First let's explain the concept of spatial continuity and the variogram.\n",
    "\n",
    "#### Spatial Continuity \n",
    "\n",
    "**Spatial Continuity** is the correlation between values over distance.\n",
    "\n",
    "* No spatial continuity – no correlation between values over distance, random values at each location in space regardless of separation distance.\n",
    "\n",
    "* Homogenous phenomenon have perfect spatial continuity, since all values as the same (or very similar) they are correlated. \n",
    "\n",
    "We need a statistic to quantify spatial continuity! A convenient method is the Semivariogram.\n",
    "\n",
    "#### The Semivariogram\n",
    "\n",
    "Function of difference over distance.\n",
    "\n",
    "* The expected (average) squared difference between values separated by a lag distance vector (distance and direction), $h$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\gamma(\\bf{h}) = \\frac{1}{2 N(\\bf{h})} \\sum^{N(\\bf{h})}_{\\alpha=1} (z(\\bf{u}_\\alpha) - z(\\bf{u}_\\alpha + \\bf{h}))^2  \n",
    "\\end{equation}\n",
    "\n",
    "where $z(\\bf{u}_\\alpha)$ and $z(\\bf{u}_\\alpha + \\bf{h})$ are the spatial sample values at tail and head locations of the lag vector respectively.\n",
    "\n",
    "* Calculated over a suite of lag distances to obtain a continuous function.\n",
    "\n",
    "* the $\\frac{1}{2}$ term converts a variogram into a semivariogram, but in practice the term variogram is used instead of semivariogram.\n",
    "* We prefer the semivariogram because it relates directly to the covariance function, $C_x(\\bf{h})$ and univariate variance, $\\sigma^2_x$:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "Note the correlogram is related to the covariance function as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\frac{C_x(\\bf{h})}{\\sigma^2_x}\n",
    "\\end{equation}\n",
    "\n",
    "The correlogram provides of function of the $\\bf{h}-\\bf{h}$ scatter plot correlation vs. lag offset $\\bf{h}$.  \n",
    "\n",
    "\\begin{equation}\n",
    "-1.0 \\le \\rho_x(\\bf{h}) \\le 1.0\n",
    "\\end{equation}\n",
    "\n",
    "#### Variogram Observations\n",
    "\n",
    "The following are common observations for variograms that should assist with their practical use.\n",
    "\n",
    "##### Observation \\#1 - As distance increases, variability increase (in general).\n",
    "\n",
    "This is common since in general, over greater distance offsets, there is often more difference between the head and tail samples.\n",
    "\n",
    "In some cases, such as with spatial cyclicity of the hole effect variogram model the variogram may have negative slope over somelag distance intervals\n",
    "\n",
    "Negative slopes at lag distances greater than half the data extent are often caused by too few pairs for a reliable variogram calculation\n",
    "\n",
    "##### Observation \\#2 - Calculated with over all possible pairs separated by lag vector, $\\bf{𝐡}$.\n",
    "\n",
    "We scan through the entire data set, searching for all possible pair combinations with all other data.  We then calculate the variogram as one half the expectation of squared difference between all pairs.\n",
    "\n",
    "More pairs results in a more reliable measure.\n",
    "\n",
    "##### Observation \\#3 - Need to plot the sill to know the degree of correlation.\n",
    "\n",
    "**Sill** is the variance, $\\sigma^2_x$\n",
    "\n",
    "Given stationarity of the variance, $\\sigma^2_x$, and variogram $\\gamma(\\bf{h})$:\n",
    "\n",
    "we can define the covariance function:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "The covariance measure is a measure of similarity over distance (the mirror image of the variogram as shown by the equation above).\n",
    "\n",
    "Given a standardized distribution $\\sigma^2_x = 1.0$, the covariance, $C_x(\\bf{h})$, is equal to the correlogram, $\\rho_x(\\bf{h})$: \n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "##### Observation \\#4 - The lag distance at which the variogram reaches the sill is know as the range.\n",
    "\n",
    "At the range, knowing the data value at the tail location provides no information about a value at the head location of the lag distance vector.\n",
    "\n",
    "##### Observation \\#5 - The nugget effect, a discontinuity at the origin\n",
    "\n",
    "Sometimes there is a discontinuity in the variogram at distances less than the minimum data spacing.  This is known as **nugget effect**.\n",
    "\n",
    "The ratio of nugget / sill, is known as relative nugget effect (%). Modeled as a discontinuity with no correlation structure that at lags, $h \\gt \\epsilon$, an infinitesimal lag distance, and perfect correlation at $\\bf{h} = 0$.\n",
    "Caution when including nuggect effect in the variogram model as measurement error, mixing populations cause apparent nugget effect\n",
    "\n",
    "This exercise demonstrates the semivariogram calculation with GeostatsPy. The steps include:\n",
    "\n",
    "1. generate a 2D model with sequential Gaussian simulation\n",
    "2. sample from the simulation\n",
    "3. calculate and visualize experimental semivariograms\n",
    "\n",
    "#### Variogram Modeling\n",
    "\n",
    "Spatial continuity can be modeled with nested, positive definate variogram structures:\n",
    "\n",
    "\\begin{equation}\n",
    "\\Gamma_x(\\bf{h}) = \\sum_{i=1}^{nst} \\gamma_i(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "where $\\Gamma_x(\\bf{h})$ is the nested variogram model resulting from the summation of $nst$ nested variograms  $\\gamma_i(\\bf{h})$.\n",
    "\n",
    "The types of structure commonly applied include:\n",
    "\n",
    "* spherical\n",
    "\n",
    "* exponential\n",
    "\n",
    "* gaussian\n",
    "\n",
    "* nugget\n",
    "\n",
    "Other less common models include:\n",
    "\n",
    "* hole effect\n",
    "\n",
    "* dampenned hole effect\n",
    "\n",
    "* power law\n",
    "\n",
    "these will not be covered here.\n",
    "\n",
    "Each one of these variogram structures, $\\gamma_i(\\bf{h})$, is based on a geometric anisotropy model parameterized by the orientation and range in the major and minor directions.  In 2D this is simply an azimuth and ranges, $azi$, $a_{maj}$ and $a_{min}$. Note, the range in the minor direction (orthogonal to the major direction).\n",
    "\n",
    "The geometric anisotropy model assumes that the range in all off-diagonal directions is based on an ellipse with the major and minor axes alligned with and set to the major and minor for the variogram.\n",
    "\n",
    "\\begin{equation}\n",
    "\\bf{h}_i = \\sqrt{\\left(\\frac{r_{maj}}{a_{maj_i}}\\right)^2 + \\left(\\frac{r_{maj}}{a_{maj_i}}\\right)^2}  \n",
    "\\end{equation}\n",
    "\n",
    "Therefore, if we know the major direction, range in major and minor directions, we may completely describe each nested componnent of the complete spatial continuity of the variable of interest, $i = 1,\\dots,nst$.\n",
    "\n",
    "Some comments on modeling nested variograms:\n",
    "\n",
    "* we can capture nugget, short and long range continuity structures\n",
    "\n",
    "* we rely on the geometric anisotropy model, so all structures must inform the same level of contribution (porportion of the sill) in all directions.\n",
    "\n",
    "* the geometric anisotropy model is based on azimuth of the major direction of continuity, range in the major direction and range in the minor direction (orthogonal to the major direction).  The range is interpolated between the major and minor azimuths with a ellipse model\n",
    "\n",
    "* we can vary the type of variogram, direction or azimuth of the major direction, and major and minor ranges by structure\n",
    "\n",
    "In this workflow we will explore methods to:\n",
    "\n",
    "1. detect directionality from a spatial dataset\n",
    "2. calculate the directional variograms in the major and minor directions \n",
    "3. build a consistent 2D model fit to the major and minor directions\n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - sample_data.csv at https://git.io/fh4gm.\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB                       # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats                 # GSLIB methods convert to Python    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os                                               # to set current working directory \n",
    "import sys                                              # supress output to screen for interactive variogram modeling\n",
    "import io\n",
    "import numpy as np                                      # arrays and matrix math\n",
    "import pandas as pd                                     # DataFrames\n",
    "import matplotlib.pyplot as plt                         # plotting\n",
    "from matplotlib.pyplot import cm                        # color maps\n",
    "from ipywidgets import interactive                      # widgets and interactivity\n",
    "from ipywidgets import widgets                            \n",
    "from ipywidgets import Layout\n",
    "from ipywidgets import Label\n",
    "from ipywidgets import VBox, HBox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"d:/PGE383\")                                   # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>259</td>\n",
       "      <td>190</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0.041122</td>\n",
       "      <td>0.120862</td>\n",
       "      <td>6367.442357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>501</td>\n",
       "      <td>270</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0.043853</td>\n",
       "      <td>0.094627</td>\n",
       "      <td>6906.176965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>273</td>\n",
       "      <td>270</td>\n",
       "      <td>219</td>\n",
       "      <td>0</td>\n",
       "      <td>0.062169</td>\n",
       "      <td>0.408779</td>\n",
       "      <td>6244.965113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>159</td>\n",
       "      <td>380</td>\n",
       "      <td>259</td>\n",
       "      <td>0</td>\n",
       "      <td>0.064295</td>\n",
       "      <td>0.322764</td>\n",
       "      <td>7113.679894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>96</td>\n",
       "      <td>870</td>\n",
       "      <td>449</td>\n",
       "      <td>0</td>\n",
       "      <td>0.065537</td>\n",
       "      <td>0.672005</td>\n",
       "      <td>5672.957304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index    X    Y  Facies  Porosity      Perm           AI\n",
       "0    259  190   19       0  0.041122  0.120862  6367.442357\n",
       "1    501  270   49       0  0.043853  0.094627  6906.176965\n",
       "2    273  270  219       0  0.062169  0.408779  6244.965113\n",
       "3    159  380  259       0  0.064295  0.322764  7113.679894\n",
       "4     96  870  449       0  0.065537  0.672005  5672.957304"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"sample_data_MV_biased.csv\")                     # read a .csv file in as a DataFrame\n",
    "#print(df.iloc[0:5,:])                                  # display first 4 samples in the table as a preview\n",
    "df.head()                                               # we could also use this command for a table preview "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will work with all facies pooled together. I wanted to simplify this workflow and focus more on spatial continuity direction detection. Finally, by not using facies we do have more samples to support our statistical inference. Most often facies are essential in the subsurface model. Don't worry we will check if this is reasonable in a bit.   \n",
    "\n",
    "You are welcome to repeat this workflow on a by-facies basis.  The following code could be used to build DataFrames ('df_sand' and 'df_shale') for each facies.\n",
    "\n",
    "```p\n",
    "df_sand = pd.DataFrame.copy(df[df['Facies'] == 1]).reset_index()  # copy only 'Facies' = sand records\n",
    "df_shale = pd.DataFrame.copy(df[df['Facies'] == 0]).reset_index() # copy only 'Facies' = shale records\n",
    "```\n",
    "\n",
    "Let's look at summary statistics for all facies combined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Index</th>\n",
       "      <td>367.0</td>\n",
       "      <td>292.926431</td>\n",
       "      <td>169.167110</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>586.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>367.0</td>\n",
       "      <td>500.108992</td>\n",
       "      <td>289.978309</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>765.000000</td>\n",
       "      <td>990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>367.0</td>\n",
       "      <td>520.403270</td>\n",
       "      <td>277.752407</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>539.000000</td>\n",
       "      <td>769.000000</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>367.0</td>\n",
       "      <td>0.596730</td>\n",
       "      <td>0.491224</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>367.0</td>\n",
       "      <td>0.126874</td>\n",
       "      <td>0.030546</td>\n",
       "      <td>0.041122</td>\n",
       "      <td>0.103398</td>\n",
       "      <td>0.125616</td>\n",
       "      <td>0.148323</td>\n",
       "      <td>0.210258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>367.0</td>\n",
       "      <td>83.313352</td>\n",
       "      <td>224.350475</td>\n",
       "      <td>0.094627</td>\n",
       "      <td>2.288119</td>\n",
       "      <td>10.294907</td>\n",
       "      <td>50.219914</td>\n",
       "      <td>1991.097723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>367.0</td>\n",
       "      <td>4790.458884</td>\n",
       "      <td>975.582304</td>\n",
       "      <td>1981.177309</td>\n",
       "      <td>4108.843701</td>\n",
       "      <td>4712.673154</td>\n",
       "      <td>5466.957467</td>\n",
       "      <td>7561.250336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count         mean         std          min          25%  \\\n",
       "Index     367.0   292.926431  169.167110     0.000000   150.000000   \n",
       "X         367.0   500.108992  289.978309     0.000000   240.000000   \n",
       "Y         367.0   520.403270  277.752407     9.000000   269.000000   \n",
       "Facies    367.0     0.596730    0.491224     0.000000     0.000000   \n",
       "Porosity  367.0     0.126874    0.030546     0.041122     0.103398   \n",
       "Perm      367.0    83.313352  224.350475     0.094627     2.288119   \n",
       "AI        367.0  4790.458884  975.582304  1981.177309  4108.843701   \n",
       "\n",
       "                  50%          75%          max  \n",
       "Index      295.000000   440.000000   586.000000  \n",
       "X          500.000000   765.000000   990.000000  \n",
       "Y          539.000000   769.000000   999.000000  \n",
       "Facies       1.000000     1.000000     1.000000  \n",
       "Porosity     0.125616     0.148323     0.210258  \n",
       "Perm        10.294907    50.219914  1991.097723  \n",
       "AI        4712.673154  5466.957467  7561.250336  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()                          # summary table of sand only DataFrame statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's transform the porosity and permeaiblity data to standard normal (mean = 0.0, standard deviation = 1.0, Gaussian shape). This is required for sequential Gaussian simulation (common target for our variogram models) and the Gaussian transform assists with outliers and provides more interpretable variograms. \n",
    "\n",
    "Let's look at the inputs for the GeostatsPy nscore program.  Note the output include an ndarray with the transformed values (in the same order as the input data in Dataframe 'df' and column 'vcol'), and the transformation table in original values and also in normal score values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.nscore(df, vcol, wcol=None, ismooth=False, dfsmooth=None, smcol=0, smwcol=0)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.nscore                                         # see the input parameters required by the nscore function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following command will transform the Porosity and Permeabilty to standard normal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform to Gaussian by Facies\n",
    "df['NPor'], tvPor, tnsPor = geostats.nscore(df, 'Porosity') # nscore transform for all facies porosity \n",
    "df['NPerm'], tvPermSand, tnsPermSand = geostats.nscore(df, 'Perm')  # nscore transform for all facies permeability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the updated DataFrame to make sure that we now have the normal score porosity and permeability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>NPor</th>\n",
       "      <th>NPerm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>259</td>\n",
       "      <td>190</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0.041122</td>\n",
       "      <td>0.120862</td>\n",
       "      <td>6367.442357</td>\n",
       "      <td>-2.671293</td>\n",
       "      <td>-2.644781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>501</td>\n",
       "      <td>270</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0.043853</td>\n",
       "      <td>0.094627</td>\n",
       "      <td>6906.176965</td>\n",
       "      <td>-2.644781</td>\n",
       "      <td>-2.775560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>273</td>\n",
       "      <td>270</td>\n",
       "      <td>219</td>\n",
       "      <td>0</td>\n",
       "      <td>0.062169</td>\n",
       "      <td>0.408779</td>\n",
       "      <td>6244.965113</td>\n",
       "      <td>-2.467028</td>\n",
       "      <td>-1.789282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>159</td>\n",
       "      <td>380</td>\n",
       "      <td>259</td>\n",
       "      <td>0</td>\n",
       "      <td>0.064295</td>\n",
       "      <td>0.322764</td>\n",
       "      <td>7113.679894</td>\n",
       "      <td>-2.344090</td>\n",
       "      <td>-1.945032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>96</td>\n",
       "      <td>870</td>\n",
       "      <td>449</td>\n",
       "      <td>0</td>\n",
       "      <td>0.065537</td>\n",
       "      <td>0.672005</td>\n",
       "      <td>5672.957304</td>\n",
       "      <td>-2.248832</td>\n",
       "      <td>-1.316486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index    X    Y  Facies  Porosity      Perm           AI      NPor  \\\n",
       "0    259  190   19       0  0.041122  0.120862  6367.442357 -2.671293   \n",
       "1    501  270   49       0  0.043853  0.094627  6906.176965 -2.644781   \n",
       "2    273  270  219       0  0.062169  0.408779  6244.965113 -2.467028   \n",
       "3    159  380  259       0  0.064295  0.322764  7113.679894 -2.344090   \n",
       "4     96  870  449       0  0.065537  0.672005  5672.957304 -2.248832   \n",
       "\n",
       "      NPerm  \n",
       "0 -2.644781  \n",
       "1 -2.775560  \n",
       "2 -1.789282  \n",
       "3 -1.945032  \n",
       "4 -1.316486  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()                                               # preview sand DataFrame with nscore transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks good! One way to check is to see if the relative magnitudes of the normal score transformed values match the original values.  e.g. that the normal score transform of 0.10 porosity normal score is less than the normal score transform of 0.14 porsity.  Also, the normal score transform of values close to the mean value should be close to 0.0 \n",
    "\n",
    "Let's also check the original and transformed sand and shale porosity distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)                                        # plot original sand and shale porosity histograms\n",
    "plt.hist(df['Porosity'], facecolor='red',bins=np.linspace(0.0,0.25,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label='Original')\n",
    "plt.xlim([0.05,0.25]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Porosity')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(222)  \n",
    "plt.hist(df['NPor'], facecolor='blue',bins=np.linspace(-3.0,3.0,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label = 'Trans')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Nscore Porosity')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(223)                                        # plot nscore transformed sand and shale histograms\n",
    "plt.hist(df['Perm'], facecolor='red',bins=np.linspace(0.0,1000.0,100000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label='Original')\n",
    "plt.xlim([0.0,1000.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Permeability')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(224)                                        # plot nscore transformed sand and shale histograms\n",
    "plt.hist(df['NPerm'], facecolor='blue',bins=np.linspace(-3.0,3.0,100000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label = 'Trans')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Permeability (mD)'); plt.ylabel('Frequency'); plt.title('Nscore Permeability')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=2.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normal score transform has correctly transformed the porosity and permeability to standard normal.\n",
    "\n",
    "#### Method \\#1: Ocular Inspection of Posted Data\n",
    "\n",
    "Data visualization is very useful to detect patterns. Our brains are very good at pattern detection. I promote quantitative methods and recognize issues with cognitive bias, but it is important to recognize the value is expert intepretation based on data visualization.\n",
    "\n",
    "* This data visualization will also be important to assist with parameter selection for the quantitative methods later.\n",
    "\n",
    "Let's plot the location maps of normal score transforms of porosity and permeability for all facies. We will also include a cross plot of the nscore permeability vs. porosity colored by facies to aid with comparison in spatial features between the porosity and permeability data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.plasma                    # color map\n",
    "plt.subplot(131)\n",
    "GSLIB.locmap_st(df,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - All Facies','X (m)','Y (m)','Nscore Porosity',cmap)\n",
    "\n",
    "plt.subplot(132)\n",
    "GSLIB.locmap_st(df,'X','Y','NPerm',0,1000,0,1000,-3,3,'Nscore Permeability - All Facies','X (m)','Y (m)','Nscore Permeability',cmap)\n",
    "\n",
    "plt.subplot(133)\n",
    "facies = df['Facies'].values +0.01\n",
    "plt.scatter(df['NPor'],df['NPerm'],c = facies,edgecolor = 'black',cmap = plt.cm.inferno)\n",
    "#plt.plot([-3,3],[-3,3],color = 'black')\n",
    "plt.xlabel(r'Nscore Porosity')\n",
    "plt.ylabel(r'Nscore Permeability')\n",
    "plt.title('Nscore Permeability vs. Porosity')\n",
    "plt.xlim([-3,3])\n",
    "plt.ylim([-3,3])\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=0.8, wspace=0.5, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you see?  Here's my observations:\n",
    "\n",
    "* there is a high degree of spatial agreement between porosity and permeability, this is supported by the high correlation evident in the cross plot.\n",
    "* there are no discontinuities that could suggest that facies represent a distinct change, rather the porosity and permeability seem continuous and the assigned facies are a truncation of their continous behavoir, we doing 'ok' with no facies\n",
    "* suspect a 045 azimuth major direction of continuity (up - right)\n",
    "* there may be cycles in the 135 azimuth \n",
    "* there will not likely be a nugget effect, but there is an hint of some short scale discontinuity?\n",
    "\n",
    "**Do you agree?** If you have a different observations, drop me a line at mpyrcz@austin.utexas.edu and I'll add to this lesson with credit.\n",
    "\n",
    "#### Method \\#2: Variogram Maps\n",
    "\n",
    "Let's try out variogram maps. \n",
    "\n",
    "* I realized that I had not coded variogram maps in Python, so I just did it and added it to GeostatsPy.  I have added if here temporarily to support my students completely their assignments. I'm teaching this week at a company and it is safer not to update the package until I have time to test it.\n",
    "\n",
    "The inputs include: \n",
    "\n",
    "* **input data** - DataFrame, 'df', and columns for x, y and property of interest, 'x', 'y' and 'vcol', \n",
    "* **variogram map parameters** - number of cells in each direction to search, 'nxlag', nylag', the cell size / lag distance, 'dxlag' and 'dylag'''\n",
    "* **search** - the minimum number of pairs reuqired to assign a result, 'mnpairs'\n",
    "* **normalization** - 1 for standardize variance to 1.0 and 0 for not\n",
    "\n",
    "The output is a 2D ndarray with the variogram map and the number of pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the output is (23, 23)\n"
     ]
    }
   ],
   "source": [
    "vmap, npmap = geostats.varmapv(df,'X','Y','NPor',tmin=-999,tmax=999,nxlag=11,nylag=11,dxlag=50,dylag=50,minnp=1,isill=1)\n",
    "\n",
    "plt.subplot(121)\n",
    "GSLIB.pixelplt_st(vmap,-575,575,-575,575,50.0,0,1.6,'Nscore Porosity Variogram Map','X(m)','Y(m)','Nscore Variogram',cmap)\n",
    "\n",
    "#plt.subplot(122)\n",
    "#GSLIB.pixelplt_st(npmap,-575,575,-575,575,50.0,0,900,'Nscore Porosity Variogram Map Number of Pairs','X(m)','Y(m)','Number of Pairs',cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()\n",
    "\n",
    "print('The shape of the output is ' + str(vmap.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the output ndarray is 23, 23 cells? We asked for the number of cells to extend in each direction, 11 and 11 in x and y.  The map has an origin (zero distance) cell in the middle and extends 11 in both positive and negative directions.  So we have 2*$nx$ + 1, 2*$ny$ + 1 cells in the resulting variogram map and the $xmin = -1 * (nx * x_{cellsize} + \\frac{1}{2} x_{cellsize})$ and the $xmax = nx * x_{cellsize} + \\frac{1}{2} x_{cellsize}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you think of this variogram map? These are my observations:\n",
    "\n",
    "* major continuity direction is at azimuth 045\n",
    "* there is a high degree of geometric anisotropy\n",
    "* there is cyclicity in the 135 direction \n",
    "* there may be some cyclicity in the 045 direction\n",
    "\n",
    "From this variogram map we can immediately see directionality in our spatial data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method \\#3: Experimental Variograms\n",
    "\n",
    "Another method for exploring spatial data directionality is the calculation of multiple experimental variograms for a variety of directions.\n",
    "\n",
    "We can use the location maps to help determine good variogram calculation parameters.\n",
    "\n",
    "```p\n",
    "tmin = -9999.; tmax = 9999.; \n",
    "lag_dist = 100.0; lag_tol = 50.0; nlag = 7; bandh = 9999.9; azi = azi; atol = 22.5; isill = 1\n",
    "```\n",
    "* **tmin**, **tmax** are trimming limits - set to have no impact, no need to filter the data\n",
    "* **lag_dist**, **lag_tol** are the lag distance, lag tolerance - set based on the common data spacing (100m) and tolerance as 100% of lag distance for additonal smoothing\n",
    "* **nlag** is number of lags - set to extend just past 50 of the data extent\n",
    "* **bandh** is the horizontal band width - set to have no effect\n",
    "* **azi** is the azimuth -  it has not effect since we set atol, the azimuth tolerance, to 90.0\n",
    "* **isill** is a boolean to standardize the distribution to a variance of 1 - it has no effect since the nscore transform sets the variance to 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmin = -9999.; tmax = 9999.                             # no trimming \n",
    "lag_dist = 100.0; lag_tol = 100.0; nlag = 8;            # maximum lag is 700m and tolerance > 1/2 lag distance for smoothing\n",
    "bandh = 9999.9; atol = 22.5                             # no bandwidth, directional variograms\n",
    "isill = 1                                               # standardize sill\n",
    "azi_mat = [0,22.5,45,67.5,90,112.5,135,157.5]           # directions in azimuth to consider"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try running these variograms and visualizing them on separate plots.  I'll demonstrate a method to promgramtically loop over each direction for efficiency (and code brevity).\n",
    "\n",
    "* we have the direction in the list called 'azi_mat'\n",
    "* we use the command:\n",
    "```p\n",
    "for iazi in range(0,len(azi_mat)): \n",
    "```\n",
    "to loop over all the elements in the list with index 'iazi'\n",
    "\n",
    "* we run the variogram calculation and store the reuslts in 2D arrays, iy is direction, ix is the lag\n",
    "* we use subplots with the 'iazi' index to add each plot\n",
    "\n",
    "```p\n",
    "    plt.subplot(4,2,iazi+1)\n",
    "```\n",
    "we add one because the plot index must be $1,\\ldots,n$, but arrays / list index as $0,\\ldots,n-1$ in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Arrays to store the results\n",
    "lag = np.zeros((len(azi_mat),nlag+2)); gamma = np.zeros((len(azi_mat),nlag+2)); npp = np.zeros((len(azi_mat),nlag+2));\n",
    "\n",
    "for iazi in range(0,len(azi_mat)):                      # Loop over all directions\n",
    "    lag[iazi,:], gamma[iazi,:], npp[iazi,:] = geostats.gamv(df,\"X\",\"Y\",\"NPor\",tmin,tmax,lag_dist,lag_tol,nlag,azi_mat[iazi],atol,bandh,isill)\n",
    "    plt.subplot(4,2,iazi+1)\n",
    "    plt.plot(lag[iazi,:],gamma[iazi,:],'x',color = 'black',label = 'Azimuth ' +str(azi_mat[iazi]))\n",
    "    plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    plt.title('Directional NSCORE Porosity Variogram')\n",
    "    plt.xlim([0,700])\n",
    "    plt.ylim([0,1.8])\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=4.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The directional variograms provide a very clear image of directionality. The ranges vary from 300m, 500m to zonal anisotropy, to 500m, 350m, 280m, 250m and finally 280m.  We are observing the actually spatial continuity ellipse by exploring a variety of directions! \n",
    "\n",
    "* We can observe that Azimuth 045 is the major direction and Azimuth 135 is the minor direction.\n",
    "\n",
    "This is a very powerful tool for exploring directionality in spatial datasets.\n",
    "\n",
    "#### Variogram Modeling\n",
    "\n",
    "From above we can identify 045 as the major direction and 135 as the minor direction. We must build a single 2D nested variogram model based on the experiential variograms in each 045 and 135 directions.  First, let's look at these two experimental variograms again.\n",
    "\n",
    "Below edit the following code to select the major and minor directions from the plots above.  \n",
    "\n",
    "```python\n",
    "# Select the plot above for the major and minor\n",
    "imajor = 2\n",
    "iminor = 6\n",
    "```\n",
    "\n",
    "Note the first plot is 0 and then step through $0, 1, \\ldots, n$ where $n$ is the number of directional variograms calculated above.  For the provided example we selected the plots, 3rd (045 Azimuth) for the major direction and the 7th (135 Azimuth) for the minor direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Major direction is 45 azimuth.\n",
      "Minor direction is 135 azimuth.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select the plot above for the major and minor\n",
    "imajor = 2\n",
    "iminor = 6\n",
    "\n",
    "print('Major direction is ' + str(azi_mat[imajor]) + ' azimuth.')\n",
    "print('Minor direction is ' + str(azi_mat[iminor]) + ' azimuth.')\n",
    "\n",
    "if not abs(azi_mat[imajor] - azi_mat[iminor]) == 90.0:\n",
    "    print('Major and minor directions must be orthogonal to each other.')\n",
    "    sys.exit()\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(lag[imajor,:],gamma[imajor,:],'x',color = 'black',label = 'Azimuth ' + str(azi_mat[imajor]))\n",
    "plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "plt.title('Directional NSCORE Porosity Variogram - Major ' + str(azi_mat[imajor]) + ' Azimuth')\n",
    "plt.xlim([0,700])\n",
    "plt.ylim([0,1.8])\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(lag[iminor,:],gamma[iminor,:],'x',color = 'black',label = 'Azimuth ' +str(azi_mat[iminor]))\n",
    "plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "plt.title('Directional NSCORE Porosity Variogram  - Minor ' + str(azi_mat[iminor]) + ' Azimuth')\n",
    "plt.xlim([0,700])\n",
    "plt.ylim([0,1.8])\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.2, top=1.6, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will assume these models will be applied in sequential Gaussian simulation; therefore, we must model to the sill (or the global distribution will not be reproduced over the realizations).  We will also not use cyclicity for now, as we are just getting started.  \n",
    "\n",
    "* Let's build a reasonable model to the sill.\n",
    "\n",
    "##### make_variogram function\n",
    "\n",
    "We use the make_variogram function to make a variogram model \n",
    "\n",
    "* a dictionary for compact storage of the variogram model parameters to pass into plotting (below), kriging and simulation methods \n",
    "\n",
    "The variogram model parameter include:\n",
    "\n",
    "* **nug** - nugget effect contribution to sill\n",
    "* **nst** - number of nested structures (1 or 2)\n",
    "* **it** - type for this nested structure (1 - spherical, 2 - exponential, 3 - Gaussian)\n",
    "* **cc** - contribution of each nested structure (contributions + nugget must sum to the sill)\n",
    "* **azi** - the azimuth for this nested structure of the major direction, the minor is orthogonal\n",
    "* **hmaj** - the range for this nested structure in the major direction\n",
    "* **hmin** - the range for this nested structure in the minor direction\n",
    "\n",
    "We increment it, cc, azi, hmaj, and hmin for the 1st and 2nd structures\n",
    "\n",
    "* for only 1 structure plus optional nugget, omit the 2nd structure parameters and they will default to $cc2 = 0$, no contribution to the model\n",
    "\n",
    "Here's my model:\n",
    "\n",
    "```p\n",
    "nug = 0.0; nst = 2\n",
    "it1 = 1; cc1 = 0.6; azi1 = 45; hmaj1 = 350; hmin1 = 350                # first structure\n",
    "it2 = 1; cc2 = 0.4; azi2 = 45; hmaj2 = 9999.9; hmin2 = 400             # second structure\n",
    "```\n",
    "\n",
    "Some comments on our model:\n",
    "\n",
    "* we model to the sill of 1.0, since we applied the normal score transform ($nug + cc1 + cc2 = 1.0$)\n",
    "\n",
    "* we used 2 spherical structures to capture zontal anisotropy in the 045 azimuth\n",
    "\n",
    "* since the experimental variogram exceeds the sill with trend or cyclicity we could have attempted trend modeling and then worked with the residual, but we will not do this for workflow brevity and simplicity\n",
    "\n",
    "We input these model parameters to make a variogram model dictionary with the make_variogram function as follows:\n",
    "\n",
    "```p\n",
    "vario = GSLIB.make_variogram(nug,nst,it1,cc1,azi1,hmaj1,hmin1,it2,cc2,azi2,hmaj2,hmin2)\n",
    "```\n",
    "\n",
    "##### vmodel function\n",
    "\n",
    "To plot the variogram we use the vmodel function to project the model in the major and minor directions\n",
    "\n",
    "The inputs for vmodel are:\n",
    "\n",
    "* **nlag** - the number of points along the variogram to calculate for the projection\n",
    "\n",
    "* **xlag** - the size of a lag for the projection\n",
    "\n",
    "* **azm** - the direction of the projection in azimuth (this is all we need since we are working in 2D)\n",
    "\n",
    "* **vario** - the variogram model dictionary from the make_variogram function (above)\n",
    "\n",
    "Note: this function is just for visualization by projecting the variogram model in a direction, so the convention is to use a very small **xlag** and large **nlag** for a high resolution display of the variogram model\n",
    "\n",
    "The outputs from the vmodel program include:\n",
    "\n",
    "* **index** - the lag number for the projection\n",
    "\n",
    "* **lag distance** - the distance offset along the projection (the **h** in the variogram plot)\n",
    "\n",
    "* **variogram** - the variogram value at the lag distance for the projection (the $\\gamma$(**h**) in the variogram plot)\n",
    "\n",
    "* **covariance function** - the covariance function at the lag distance for the projection (for the C(**h**) plot)\n",
    "\n",
    "* **correlogram** - the correlogram at the lag distance for the projection (for the $\\rho$(**h**) plot)\n",
    "\n",
    "We have 2 structures and no nugget effect.  We needed the 2nd structure to capture the zonal anisotropy in the 045 direction.  Let's calculate the variogram model in these directions and plot them with the experimental variograms.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " x,y,z offsets = 7.071067805519558,7.071067818211393\n",
      " x,y,z offsets = 7.071067830903227,-7.071067792827723\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nug = 0.0; nst = 2                                             # 2 nest structure variogram model parameters\n",
    "it1 = 1; cc1 = 0.6; azi1 = azi_mat[imajor]; hmaj1 = 350; hmin1 = 350\n",
    "it2 = 1; cc2 = 0.4; azi2 = azi_mat[imajor]; hmaj2 = 9999.9; hmin2 = 400\n",
    "\n",
    "vario = GSLIB.make_variogram(nug,nst,it1,cc1,azi1,hmaj1,hmin1,it2,cc2,azi2,hmaj2,hmin2) # make model object\n",
    "nlag = 70; xlag = 10;                                          # project the model in the 045 azimuth\n",
    "index_maj,h_maj,gam_maj,cov_maj,ro_maj = geostats.vmodel(nlag,xlag,azi_mat[imajor],vario)                                                     # project the model in the 135 azimuth\n",
    "index_min,h_min,gam_min,cov_min,ro_min = geostats.vmodel(nlag,xlag,azi_mat[iminor],vario)\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(lag[imajor,:],gamma[imajor,:],'x',color = 'black',label = 'Azimuth ' +str(azi_mat[imajor]))\n",
    "plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "plt.plot(h_maj,gam_maj,color = 'black')\n",
    "plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "plt.title('Directional NSCORE Porosity Variogram - Major ' + str(azi_mat[imajor]) + ' Azimuth')\n",
    "plt.xlim([0,700])\n",
    "plt.ylim([0,1.8])\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(lag[iminor,:],gamma[iminor,:],'x',color = 'black',label = 'Azimuth ' +str(azi_mat[6]))\n",
    "plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "plt.plot(h_min,gam_min,color = 'black')\n",
    "plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "plt.title('Directional NSCORE Porosity Variogram  - Minor ' + str(azi_mat[iminor]) + ' Azimuth')\n",
    "plt.xlim([0,700])\n",
    "plt.ylim([0,1.8])\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.2, top=1.6, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interactive Variogram Modeling\n",
    "\n",
    "The following code uses the ipywidgets and matplotlib packages to build a method for interactive variogram modeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interactive calculation of the sample set (control of source parametric distribution and number of samples)\n",
    "l = widgets.Text(value='                              Variogram Modeling, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "nug = widgets.FloatSlider(min = 0, max = 1.0, value = 0.0, step = 0.1, description = 'nug',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "nug.style.handle_color = 'gray'\n",
    "it1 = widgets.Dropdown(options=['Spherical', 'Exponential', 'Gaussian'],value='Spherical',\n",
    "    description='Type1:',disabled=False,layout=Layout(width='200px', height='30px'))\n",
    "c1 = widgets.FloatSlider(min=0.0, max = 1.0, value = 0.0, description = 'c1',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "c1.style.handle_color = 'blue'\n",
    "hmaj1 = widgets.FloatSlider(min=0.01, max = 10000.0, value = 0.01, step = 100.0, description = 'hmaj1',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "hmaj1.style.handle_color = 'red'\n",
    "hmin1 = widgets.FloatSlider(min = 0.01, max = 10000.0, value = 0.01, step = 100.0, description = 'hmin1',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "hmin1.style.handle_color = 'green'\n",
    "\n",
    "it2 = widgets.Dropdown(options=['Spherical', 'Exponential', 'Gaussian'],value='Spherical',\n",
    "    description='Type2:',disabled=False,layout=Layout(width='200px', height='30px'))\n",
    "c2 = widgets.FloatSlider(min=0.0, max = 1.0, value = 0.0, description = 'c2',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "c2.style.handle_color = 'blue'\n",
    "hmaj2 = widgets.FloatSlider(min=0.01, max = 10000.0, value = 0.01, step = 100.0, description = 'hmaj2',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "hmaj2.style.handle_color = 'red'\n",
    "hmin2 = widgets.FloatSlider(min = 0.01, max = 10000.0, value = 0.01, step = 100.0, description = 'hmin2',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "hmin2.style.handle_color = 'green'\n",
    "\n",
    "ui1 = widgets.HBox([nug,it1,c1,hmaj1,hmin1,it2,c2,hmaj2,hmin2],)                   # basic widget formatting   \n",
    "#ui2 = widgets.HBox([it2,c2,hmaj2,hmin2],)                   # basic widget formatting   \n",
    "ui = widgets.VBox([l,ui1],)\n",
    "\n",
    "def convert_type(it):\n",
    "    if it == 'Spherical': \n",
    "        return 1\n",
    "    elif it == 'Exponential':\n",
    "        return 2\n",
    "    else: \n",
    "        return 3\n",
    "\n",
    "def f_make(nug,it1,c1, hmaj1, hmin1, it2, c2, hmaj2, hmin2):                       # function to take parameters, make sample and plot\n",
    "    text_trap = io.StringIO()\n",
    "    sys.stdout = text_trap\n",
    "    \n",
    "    it1 = convert_type(it1); it2 = convert_type(it2)\n",
    "    if c2 > 0.0:\n",
    "        nst = 2\n",
    "    else:\n",
    "        nst = 1\n",
    "    \n",
    "    vario = GSLIB.make_variogram(nug,nst,it1,c1,azi_mat[imajor],hmaj1,hmin1,it2,c2,azi2,hmaj2,hmin2) # make model object\n",
    "    nlag = 70; xlag = 10;                                     \n",
    "    index_maj,h_maj,gam_maj,cov_maj,ro_maj = geostats.vmodel(nlag,xlag,azi_mat[imajor],vario)   # project the model in the major azimuth                                                  # project the model in the 135 azimuth\n",
    "    index_min,h_min,gam_min,cov_min,ro_min = geostats.vmodel(nlag,xlag,azi_mat[iminor],vario) # project the model in the minor azimuth\n",
    "    \n",
    "    plt.subplot(2,2,1)\n",
    "    plt.plot(lag[imajor,:],gamma[imajor,:],'x',color = 'black',label = 'Azimuth ' +str(azi_mat[2]))\n",
    "    plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "    plt.plot(h_maj,gam_maj,color = 'black')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    plt.title('Horizontal Major Directional NSCORE Porosity Variogram - Major ' + str(azi_mat[imajor]) + 'Azimuth')\n",
    "    plt.xlim([0,700])\n",
    "    plt.ylim([0,1.8])\n",
    "    plt.legend(loc='upper left')\n",
    "    \n",
    "    plt.subplot(2,2,2)\n",
    "    plt.plot(lag[iminor,:],gamma[iminor,:],'x',color = 'black',label = 'Azimuth ' +str(azi_mat[6]))\n",
    "    plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "    plt.plot(h_min,gam_min,color = 'black')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    plt.title('Horizontal Minor Directional NSCORE Porosity Variogram - Minor ' + str(azi_mat[iminor]) + 'Azimuth')\n",
    "    plt.xlim([0,700])\n",
    "    plt.ylim([0,1.8])\n",
    "    plt.legend(loc='upper left')\n",
    "    \n",
    "    plt.subplot(2,2,3)\n",
    "    plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "    plt.plot(h_maj,gam_maj,color = 'black',label = 'Azimuth ' + str(azi_mat[imajor]))    \n",
    "    plt.plot(h_min,gam_min,color = 'black',label = 'Azimuth ' + str(azi_mat[iminor]))\n",
    "    deltas = [22.5, 45, 67.5]; \n",
    "    ndelta = len(deltas); hd = np.zeros(ndelta); gamd = np.zeros(ndelta);\n",
    "    color=iter(cm.plasma(np.linspace(0,1,ndelta)))\n",
    "    for delta in deltas:\n",
    "        index,hd,gamd,cov,ro = geostats.vmodel(nlag,xlag,azi_mat[imajor]+delta,vario);\n",
    "        c=next(color)\n",
    "        plt.plot(hd,gamd,color = c,label = 'Azimuth ' + str(azi_mat[imajor]+delta))\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    plt.title('Interpolated Azimuth NSCORE Porosity Variogram Models')\n",
    "    plt.xlim([0,700])\n",
    "    plt.ylim([0,1.8])\n",
    "    plt.legend(loc='upper left')\n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=2.2, top=1.5, wspace=0.3, hspace=0.3)\n",
    "    plt.show()\n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(f_make, {'nug':nug, 'it1':it1,'c1':c1, 'hmaj1':hmaj1, 'hmin1':hmin1, 'it2':it2, 'c2':c2, 'hmaj2':hmaj2, 'hmin2':hmin2})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Nested Variogram Modeling Demostration\n",
    "\n",
    "* select the nested structures and their types, contributions and major and minor ranges \n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "### The Problem\n",
    "\n",
    "Fit a positive definite variogram model based on the addition of multiple structures each describing spatial components of the feature variance \n",
    "\n",
    "* **nug**: nugget effect\n",
    "\n",
    "* **c1 / c2**: contributions of the sill\n",
    "\n",
    "* **hmaj1 / hmaj2**: range in the major direction\n",
    "\n",
    "* **hmin1 / hmin2**: range in the minor direction"
   ]
  },
  {
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       "VBox(children=(Text(value='                              Variogram Modeling, Michael Pyrcz, Associate Professo…"
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       "Output()"
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   "source": [
    "display(ui, interactive_plot)                            # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of vairogram modeling for spatial continuity analysis. Much more could be done, I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)  \n",
    "  "
   ]
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